{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 卷积神经网络（CNN）——CIFAR-10"
   ],
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   },
   "id": "de6b74bb6e3a67d"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-10T14:08:59.975387Z",
     "start_time": "2025-06-10T14:08:59.970843Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision.transforms as transforms\n",
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import time\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 数据集路径\n",
    "cifar10_data_dir = r'D:\\Machine_learning\\jiqixuexi\\cifar-10-python\\cifar-10-batches-py'\n",
    "# 定义device变量，优先使用GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# 加载 CIFAR-10 数据集\n",
    "def load_cifar10(data_dir):\n",
    "    # 加载 CIFAR-10 数据集的函数\n",
    "    def unpickle(file):\n",
    "        with open(file, 'rb') as fo:\n",
    "            dict = pickle.load(fo, encoding='bytes')\n",
    "        return dict\n",
    "\n",
    "    # 加载训练数据\n",
    "    train_data = []\n",
    "    train_labels = []\n",
    "    for i in range(1, 6):\n",
    "        batch_file = os.path.join(data_dir, f'data_batch_{i}')\n",
    "        batch = unpickle(batch_file)\n",
    "        train_data.append(batch[b'data'])\n",
    "        train_labels.extend(batch[b'labels'])\n",
    "    train_data = np.array(train_data).reshape(-1, 3, 32, 32)\n",
    "    train_labels = np.array(train_labels)\n",
    "\n",
    "    # 加载测试数据\n",
    "    test_file = os.path.join(data_dir, 'test_batch')\n",
    "    test_batch = unpickle(test_file)\n",
    "    test_data = np.array(test_batch[b'data']).reshape(-1, 3, 32, 32)\n",
    "    test_labels = np.array(test_batch[b'labels'])\n",
    "\n",
    "    # 归一化数据\n",
    "    train_data = train_data / 255.0\n",
    "    test_data = test_data / 255.0\n",
    "\n",
    "    return train_data, test_data, train_labels, test_labels\n",
    "\n",
    "# 自定义 CIFAR-10 数据集类\n",
    "class CustomCIFAR10Dataset(Dataset):\n",
    "    classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "    def __init__(self, images, labels, transform=None):\n",
    "        self.images = images\n",
    "        self.labels = labels\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.images)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image = self.images[idx]\n",
    "        label = self.labels[idx]\n",
    "        # 将图像转换为PyTorch张量\n",
    "        image = torch.from_numpy(image).float()\n",
    "        label = torch.from_numpy(np.array(label)).long()  \n",
    "        return image, label\n",
    "\n",
    "# 加载数据集\n",
    "train_data, test_data, train_labels, test_labels = load_cifar10(cifar10_data_dir)\n",
    "\n",
    "# 定义数据预处理\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))\n",
    "])\n",
    "\n",
    "# 创建自定义数据集\n",
    "train_dataset = CustomCIFAR10Dataset(train_data, train_labels, transform=transform)\n",
    "test_dataset = CustomCIFAR10Dataset(test_data, test_labels, transform=transform)\n",
    "\n",
    "# 创建数据加载器\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-10T14:09:01.234135Z",
     "start_time": "2025-06-10T14:09:00.534344Z"
    }
   },
   "id": "9cfc08d52ecef774",
   "execution_count": 24
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10], Loss: 1.5585\n",
      "Epoch [2/10], Loss: 1.1687\n",
      "Epoch [3/10], Loss: 0.9864\n",
      "Epoch [4/10], Loss: 0.8697\n",
      "Epoch [5/10], Loss: 0.7828\n",
      "Epoch [6/10], Loss: 0.7111\n",
      "Epoch [7/10], Loss: 0.6528\n",
      "Epoch [8/10], Loss: 0.6022\n",
      "Epoch [9/10], Loss: 0.5532\n",
      "Epoch [10/10], Loss: 0.5161\n",
      "Training Time: 47.31 seconds\n"
     ]
    }
   ],
   "source": [
    "# 定义适用于CIFAR-10的CNN模型\n",
    "class CIFAR10CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CIFAR10CNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, 3, padding=1) \n",
    "        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)\n",
    "        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc1 = nn.Linear(128 * 4 * 4, 512)  # 适应CIFAR-10的尺寸变化\n",
    "        self.fc2 = nn.Linear(512, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(torch.relu(self.conv1(x)))\n",
    "        x = self.pool(torch.relu(self.conv2(x)))\n",
    "        x = self.pool(torch.relu(self.conv3(x)))\n",
    "        x = x.view(-1, 128 * 4 * 4)  # CIFAR-10图像为32x32\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# 初始化模型、损失函数和优化器，并将模型移动到device\n",
    "model = CIFAR10CNN().to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 记录训练时间\n",
    "start_time = time.time()\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 10\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    epoch_loss = 0.0\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        epoch_loss += loss.item()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss / len(train_loader):.4f}')\n",
    "train_time = time.time() - start_time\n",
    "print(f\"Training Time: {train_time:.2f} seconds\")"
   ],
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    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-10T14:09:49.139665Z",
     "start_time": "2025-06-10T14:09:01.801703Z"
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   },
   "id": "296fbcc01f270cad",
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.7521\n",
      "Inference Time: 0.97 seconds\n"
     ]
    }
   ],
   "source": [
    "# 测试模型\n",
    "model.eval()\n",
    "start_inference = time.time()  # 开始记录推理时间\n",
    "\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    all_labels = []\n",
    "    all_preds = []\n",
    "    for images, labels in test_loader:\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(images)\n",
    "        predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        all_labels.extend(labels.cpu().numpy())\n",
    "        all_preds.extend(predicted.cpu().numpy())\n",
    "    test_acc = correct / total\n",
    "    print(f\"Test Accuracy: {test_acc:.4f}\")\n",
    "\n",
    "inference_time = time.time() - start_inference\n",
    "print(f\"Inference Time: {inference_time:.2f} seconds\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-10T14:09:54.869115Z",
     "start_time": "2025-06-10T14:09:53.895903Z"
    }
   },
   "id": "58b8dac086d68588",
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7521\n",
      "Precision: 0.7584\n",
      "Recall: 0.7521\n",
      "F1-score: 0.7533\n",
      "Training Time: 47.31 seconds\n",
      "Inference Time: 0.97 seconds\n",
      "\n",
      "Confusion Matrix:\n",
      " [[840  12  30  15   5   4   2  10  45  37]\n",
      " [ 20 875   3  11   3   1   3   0  20  64]\n",
      " [ 87   9 642  72  47  62  42  23  11   5]\n",
      " [ 35   7  54 641  32 146  32  25  16  12]\n",
      " [ 23   4  91  85 647  40  42  56  12   0]\n",
      " [ 17   3  29 188  28 674   5  38   7  11]\n",
      " [ 16   8  44 106  28  24 753   7  11   3]\n",
      " [ 25   1  35  54  33  60   3 776   2  11]\n",
      " [ 63  28  11  11   3   5   3   2 850  24]\n",
      " [ 35  84   6  13   1   4   2  11  21 823]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.72      0.84      0.78      1000\n",
      "         car       0.85      0.88      0.86      1000\n",
      "        bird       0.68      0.64      0.66      1000\n",
      "         cat       0.54      0.64      0.58      1000\n",
      "        deer       0.78      0.65      0.71      1000\n",
      "         dog       0.66      0.67      0.67      1000\n",
      "        frog       0.85      0.75      0.80      1000\n",
      "       horse       0.82      0.78      0.80      1000\n",
      "        ship       0.85      0.85      0.85      1000\n",
      "       truck       0.83      0.82      0.83      1000\n",
      "\n",
      "    accuracy                           0.75     10000\n",
      "   macro avg       0.76      0.75      0.75     10000\n",
      "weighted avg       0.76      0.75      0.75     10000\n"
     ]
    }
   ],
   "source": [
    "# 计算评估指标\n",
    "accuracy = accuracy_score(all_labels, all_preds)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average='weighted')\n",
    "conf_matrix = confusion_matrix(all_labels, all_preds)\n",
    "class_report = classification_report(all_labels, all_preds, target_names=CustomCIFAR10Dataset.classes)\n",
    "\n",
    "# 输出结果\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "print(f\"F1-score: {f1:.4f}\")\n",
    "print(f\"Training Time: {train_time:.2f} seconds\")\n",
    "print(f\"Inference Time: {inference_time:.2f} seconds\")\n",
    "print(\"\\nConfusion Matrix:\\n\", conf_matrix)\n",
    "print(\"\\nClassification Report:\\n\", class_report)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-10T14:09:56.931124Z",
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   },
   "id": "d3716ca41a6a902c",
   "execution_count": 27
  },
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   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x1000 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=train_dataset.classes, yticklabels=train_dataset.classes)\n",
    "plt.title(\"CNN - Confusion Matrix (CIFAR-10)\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-10T14:10:02.782426Z",
     "start_time": "2025-06-10T14:10:01.939855Z"
    }
   },
   "id": "5f4588001a837112",
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "fc90f6ae0573c5b0"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
